Is There a Role for Oral or Intravenous Ascorbate (Vitamin C) in Treating Patients With Cancer? A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Many cancer patients receive supplemental ascorbate (vitamin C) in the belief that it synergizes the anticancer effects of chemotherapy and reduces its toxicity. METHODS: A systematic review was performed to evaluate the antitumor effects and toxicity of ascorbate treatment. Medline (1946 to March 2014), EMBASE (1947 to March 2014), and the Cochrane central register (1993 to March 2014) were searched for randomized and observational studies. RESULTS: Of 696 identified records, 61 full-text articles were screened and 34 were included. In total, 5 randomized controlled trials (RCTs) (n = 322), 12 phase I/II trials (n = 287), 6 observational studies (n = 7,599), and 11 case reports (n = 267) were identified. Because of study heterogeneity, no meta-analyses were performed. No RCTs reported any statistically significant improvements in overall or progression-free survival or reduced toxicity with ascorbate relative to control arm. Evidence for ascorbate's antitumor effects was limited to case reports and observational and uncontrolled studies. CONCLUSION: There is no high-quality evidence to suggest that ascorbate supplementation in cancer patients either enhances the antitumor effects of chemotherapy or reduces its toxicity. Given the high financial and time costs to patients of this treatment, high-quality placebo-controlled trials are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".